Node-based architecture (NBA) designs for future satellite projects hold the promise of decreasing system development time and costs, size, weight, and power and positioning the laboratory to address other emerging mission opportunities quickly. Reconfigurable Field Programmable Gate Array (FPGA) based modules will comprise the core of several of the NBA nodes. Microprocessing capabilities will be necessary with varying degrees of mission-specific performance requirements on these nodes. To enable the flexibility of these reconfigurable nodes, it is advantageous to incorporate the microprocessor into the FPGA itself, either as a hardcore processor built into the FPGA or as a soft-core processor built out of FPGA elements. This document describes the evaluation of three reconfigurable FPGA based processors for use in future NBA systems--two soft cores (MicroBlaze and non-fault-tolerant LEON) and one hard core (PowerPC 405). Two standard performance benchmark applications were developed for each processor. The first, Dhrystone, is a fixed-point operation metric. The second, Whetstone, is a floating-point operation metric. Several trials were run at varying code locations, loop counts, processor speeds, and cache configurations. FPGA resource utilization was recorded for each configuration. Cache configurations impacted the results greatly; for optimal processor efficiency it is necessary to enable caches on the processors. Processor caches carry a penalty; cache error mitigation is necessary when operating in a radiation environment.
Date: September 1, 2008
Creator: Van Houten, Jonathan Roger; Jarosz, Jason P.; Welch, Benjamin James; Gallegos, Daniel E. & Learn, Mark Walter
Placement is one of the most important steps in physical design for VLSI circuits. For field programmable gate arrays (FPGAs), the placement step determines the location of each logic block. I present novel timing and congestion driven placement algorithms for FPGAs with minimal runtime overhead. By predicting the post-routing timing-critical edges and estimating congestion accurately, this algorithm is able to simultaneously reduce the critical path delay and the minimum number of routing tracks. The core of the algorithm consists of a criticality-history record of connection edges and a congestion map. This approach is applied to the 20 largest Microelectronics Center of North Carolina (MCNC) benchmark circuits. Experimental results show that compared with the state-of-the-art FPGA place and route package, the Versatile Place and Route (VPR) suite, this algorithm yields an average of 8.1% reduction (maximum 30.5%) in the critical path delay and 5% reduction in channel width. Meanwhile, the average runtime of the algorithm is only 2.3X as of VPR.